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The recursive deterministic perceptron neural network
Neural Networks, 1998We introduce a feedforward multilayer neural network which is a generalization of the single layer perceptron topology (SLPT), called recursive deterministic perceptron (RDP). This new model is capable of solving any two-class classification problem, as opposed to the single layer perceptron which can only solve classification problems dealing with ...
Mohamed, Tajine, David, Elizondo
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Deterministic Capacity of Networks
2007 IEEE Information Theory Workshop, 2007In this paper we define the deterministic capacity of a network. The deterministic capacity is defined by restricting nodes to transmitting (asymptotically) deterministic functions of the messages in the network. It can be seen as an upper bound on the set of rates achieved by all possible decode- forward coding schemes, in an abstract sense.
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Deterministic passive optical networking
Journal of Optical Communications and NetworkingA time-division-multiplexed (TDM) passive optical network (PON) that behaves like multiple point-to-point (P2P) links can support applications with stringent timing requirements while also maintaining the cost benefits of the point-to-multipoint (P2MP) topology.
Kostas Christodoulopoulos +4 more
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Farewell to deterministic networks
2012 19th IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), 2012As communication networks become increasingly complex and dynamic, the three functions known as monitoring, control and management prove to be ineffective. It is increasingly difficult to operate large networks, perform diagnostics, prevent cascading failures, or deliver dependable services.
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Towards Deterministic Network Coding in Hierarchical Networks
2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation, 2014Starting from a recently proposed network coding resource optimization method, we discuss the scalability of this solution to larger scale-free topologies that are also characterized by a realistic link loss distribution which we derive from measurements.
Oana Graur, Werner Henkel
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Making stochastic networks deterministic
1997Graphical models are considered more and more as a key technique for describing the dependency relations of random variables. Various learning and inference algorithms have been described and analysed. This article demonstrates how an important subclass of graphical models can be treated by transforming the underlying model into a regular feedforward ...
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Deterministic Sampling Algorithms for Network Design
Algorithmica, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
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